Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	29,953
2	Florida	27,001
3	Arizona	26,780
4	Mississippi	24,597
5	Alabama	22,508
6	New York	22,170
7	New Jersey	21,384
8	Georgia	21,161
9	South Carolina	20,912
10	Nevada	20,372
11	Texas	19,906
12	Rhode Island	19,532
13	Tennessee	19,445
14	District of Columbia	18,880
15	Massachusetts	17,999
16	Arkansas	17,723
17	Delaware	17,040
18	Iowa	16,862
19	Maryland	16,835
20	Illinois	16,714
21	California	16,210
22	Idaho	15,940
23	Nebraska	15,935
24	Utah	14,749
25	Connecticut	14,376
26	North Carolina	14,029
27	Virginia	12,686
28	Oklahoma	12,465
29	Indiana	12,447
30	Wisconsin	12,283
31	Kansas	12,230
32	South Dakota	11,804
33	Minnesota	11,721
34	North Dakota	11,529
35	Missouri	11,519
36	New Mexico	11,245
37	Michigan	10,366
38	Pennsylvania	10,177
39	Kentucky	9,461
40	Ohio	9,403
41	Washington	9,328
42	Colorado	9,320
43	Puerto Rico	8,677
44	Alaska	7,074
45	Wyoming	5,805
46	Oregon	5,615
47	Montana	5,489
48	New Hampshire	5,160
49	West Virginia	4,871
50	Hawaii	3,761
51	Maine	3,134
52	Vermont	2,451

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Texas	280
2	Nevada	227
3	Puerto Rico	210
4	Georgia	198
5	Tennessee	196
6	Alabama	189
7	Missouri	179
8	Louisiana	171
9	Arkansas	165
10	Mississippi	162
11	Idaho	161
12	Florida	159
13	California	156
14	Kansas	153
15	North Dakota	147
16	Illinois	139
17	Oklahoma	128
18	Hawaii	123
19	South Dakota	122
20	Iowa	120
21	Kentucky	118
22	South Carolina	115
23	Rhode Island	112
24	Wisconsin	112
25	Indiana	110
26	Arizona	103
27	Alaska	101
28	North Carolina	101
29	Nebraska	100
30	Virginia	98
31	Minnesota	97
32	Utah	90
33	Maryland	85
34	Washington	82
35	District of Columbia	78
36	Wyoming	76
37	Delaware	67
38	Ohio	64
39	Montana	63
40	Michigan	56
41	Oregon	52
42	West Virginia	50
43	Colorado	46
44	Pennsylvania	45
45	New Mexico	44
46	Massachusetts	41
47	Connecticut	33
48	New York	28
49	New Jersey	24
50	Maine	17
51	Vermont	11
52	New Hampshire	9

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,792
2	New York	1,667
3	Massachusetts	1,283
4	Connecticut	1,249
5	Louisiana	979
6	Rhode Island	966
7	District of Columbia	848
8	Mississippi	715
9	Michigan	661
10	Illinois	632
11	Arizona	623
12	Delaware	608
13	Maryland	603
14	Pennsylvania	590
15	Indiana	470
16	South Carolina	455
17	Florida	454
18	Georgia	441
19	Alabama	394
20	Texas	372
21	Nevada	357
22	New Mexico	344
23	Ohio	331
24	Colorado	330
25	Iowa	317
26	Minnesota	313
27	New Hampshire	311
28	California	291
29	Virginia	280
30	Washington	248
31	Missouri	240
32	North Carolina	231
33	Tennessee	206
34	Arkansas	205
35	Kentucky	192
36	Nebraska	191
37	Wisconsin	182
38	South Dakota	174
39	North Dakota	173
40	Oklahoma	172
41	Idaho	158
42	Kansas	142
43	Utah	116
44	Puerto Rico	108
45	Maine	94
46	Oregon	94
47	Vermont	92
48	West Virginia	91
49	Montana	78
50	Wyoming	58
51	Alaska	36
52	Hawaii	28

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	8
2	Florida	6
3	Mississippi	5
4	South Carolina	5
5	Texas	4
6	Georgia	3
7	Nevada	3
8	North Dakota	3
9	Tennessee	3
10	Alabama	2
11	Arkansas	2
12	California	2
13	Idaho	2
14	Iowa	2
15	Kentucky	2
16	Missouri	2
17	Oklahoma	2
18	Washington	2
19	Wyoming	2
20	Arizona	1
21	Illinois	1
22	Indiana	1
23	Kansas	1
24	Massachusetts	1
25	Minnesota	1
26	Nebraska	1
27	New Mexico	1
28	North Carolina	1
29	Ohio	1
30	Puerto Rico	1
31	Alaska	0
32	Colorado	0
33	Connecticut	0
34	Delaware	0
35	District of Columbia	0
36	Hawaii	0
37	Maine	0
38	Maryland	0
39	Michigan	0
40	Montana	0
41	New Hampshire	0
42	New Jersey	0
43	New York	0
44	Oregon	0
45	Pennsylvania	0
46	Rhode Island	0
47	South Dakota	0
48	Utah	0
49	Vermont	0
50	Virginia	0
51	West Virginia	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	140,642	1	99
Lafayette	Florida	122,299	2	99
Lake	Tennessee	114,738	3	99
Lee	Arkansas	110,986	4	99
Lincoln	Arkansas	104,039	5	99
Davidson	Tennessee	35,315	148	95
Richland	South Carolina	22,773	404	87
Orange	California	13,922	920	70
York	South Carolina	13,695	945	69
Pierce	Washington	7,548	1702	45

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,375	1	99
Galax city	Virginia	4,254	2	99
Randolph	Georgia	3,836	3	99
Terrell	Georgia	3,517	4	99
Neshoba	Mississippi	3,297	5	99
Richland	South Carolina	428	648	79
Davidson	Tennessee	333	841	73
Orange	California	257	1049	66
Pierce	Washington	176	1340	57
York	South Carolina	114	1656	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.